Cyber threats in online banking services are increasing with the improvements in internet-aided communications and extended services. The threats gather personal and transactional information from the users from which illegitimate services are handled. For providing secure service-based risk mitigation, this article introduces a Volatile Transaction Authentication Insurance Method (VTAIM). This method generates volatile insurance authenticity for a transaction-initiated session. Depending on the transaction features, the insurance is constructed using a two-way volatile authentication key. This key extension/ validity is recommended through deep learning based on the user transaction interest. The type of security threat is first detected from the session interruptions and converging transaction period. Post the detection, the user and banking service-oriented authentication is used for ensuring end-to-end security. This recommendation is used for typical consecutive transactions and volatile security. The security features are updated periodically based on service availability and transaction support provided. Therefore, spoofed services are less available for distinct users across different time intervals, reducing the false rate and failures.